import torch  
import torch.nn as nn  
import torch.optim as optim  
import torch.nn.functional as F  
import numpy as np  
from sklearn.cluster import KMeans  
from scipy.spatial.distance import cosine  

class SE_CC(nn.Module):  
    def __init__(self, backbone, num_classes, num_clusters):  
        super(SE_CC, self).__init__()  
        
        # 主干网络  
        self.backbone = backbone  
        
        # 教师模型和学生模型  
        self.teacher_model = nn.Sequential(  
            backbone,  
            nn.Linear(backbone.feature_dim, num_classes)  
        )  
        self.student_model = nn.Sequential(  
            backbone,  
            nn.Linear(backbone.feature_dim, num_classes)  
        )  
        
        # 聚类分支  
        self.clustering_branch = nn.Linear(backbone.feature_dim, num_clusters)  
        
        # 超参数  
        self.num_classes = num_classes  
        self.num_clusters = num_clusters  
        self.temperature = 0.1  
        
        # 初始化聚类  
        self.cluster_centroids = None  
        
    def perform_clustering(self, target_features):  
        # 使用 K-means 进行聚类  
        kmeans = KMeans(n_clusters=self.num_clusters)  
        cluster_labels = kmeans.fit_predict(target_features.cpu().numpy())  
        self.cluster_centroids = torch.from_numpy(kmeans.cluster_centers_).to(target_features.device)  
        return torch.from_numpy(cluster_labels).to(target_features.device)  
    
    def compute_inherent_cluster_distribution(self, features):  
        # 计算样本与聚类中心的余弦相似度  
        similarities = torch.zeros(features.size(0), self.num_clusters).to(features.device)  
        for k in range(self.num_clusters):  
            similarities[:, k] = 1 - torch.tensor([cosine(f.cpu().numpy(), self.cluster_centroids[k].cpu().numpy())   
                                                   for f in features])  
        
        # 应用 softmax  
        return F.softmax(similarities / self.temperature, dim=1)  
    
    def forward(self, source_data, target_data):  
        # 源域数据监督学习  
        source_features = self.backbone(source_data)  
        source_logits = self.student_model[1](source_features)  
        source_loss = F.cross_entropy(source_logits, source_labels)  
        
        # 目标域数据自集成学习  
        target_features_student = self.backbone(target_data)  
        target_features_teacher = self.backbone(target_data)  
        
        # 聚类分支  
        cluster_assignments = self.clustering_branch(target_features_student)  
        inherent_distribution = self.compute_inherent_cluster_distribution(target_features_student)  
        
        # KL散度损失  
        kl_loss = F.kl_div(  
            F.log_softmax(cluster_assignments, dim=1),   
            inherent_distribution,   
            reduction='batchmean'  
        )  
        
        # 互信息最大化损失  
        mutual_info_loss = self.compute_mutual_information_loss(  
            target_features_student,   
            cluster_assignments,   
            teacher_predictions  
        )  
        
        # 总损失  
        total_loss = (  
            source_loss +   
            self.self_ensembling_loss(target_data) +   
            kl_loss +   
            mutual_info_loss  
        )  
        
        return total_loss  
    
    def self_ensembling_loss(self, target_data):  
        # 对目标域数据进行自集成损失计算  
        augmented_data1 = self.augment(target_data)  
        augmented_data2 = self.augment(target_data)  
        
        student_pred1 = self.student_model(augmented_data1)  
        student_pred2 = self.student_model(augmented_data2)  
        
        return F.mse_loss(student_pred1, student_pred2)  
    
    def compute_mutual_information_loss(self, features, cluster_assignments, class_predictions):  
        # 计算特征、聚类和类别分布之间的互信息损失  
        # 这里是一个简化的实现，实际论文中的互信息计算更复杂  
        mi_loss = (  
            self.global_mutual_info(features, cluster_assignments) +  
            self.local_mutual_info(features, class_predictions)  
        )  
        return mi_loss  
    
    def global_mutual_info(self, features, distributions):  
        # 全局互信息估计  
        return torch.mean(torch.sum(distributions * torch.log(distributions + 1e-10), dim=1))  
    
    def local_mutual_info(self, features, predictions):  
        # 局部互信息估计  
        return torch.mean(torch.sum(predictions * torch.log(predictions + 1e-10), dim=1))  
    
    def augment(self, data):  
        # 数据增强方法  
        # 可以使用随机裁剪、翻转、颜色抖动等  
        return data  

# 初始化模型和优化器  
backbone = ResNet50()  # 假设有一个 ResNet50 主干网络  
model = SE_CC(backbone, num_classes=12, num_clusters=25)  
optimizer = optim.SGD(model.parameters(), lr=0.001)  

# 训练循环  
for epoch in range(num_epochs):  
    for source_batch, target_batch in dataloader:  
        optimizer.zero_grad()  
        loss = model(source_batch, target_batch)  
        loss.backward()  
        optimizer.step()  

